Convergence-HPC-BD-ML-JointWSreport-2019-slide

THE CONVERGENCE OF HIGH PERFORMANCE COMPUTING, BIG DATA, AND MACHINE LEARNING

(September 9, 2019)

The high performance computing (HPC) and big data (BD) communities are evolving in response to changing user needs and technological landscapes. Researchers are increasingly using machine learning (ML) not only for data analytics but also for modeling and simulation; science-based simulations are increasingly relying on embedded ML models not only to interpret results from massive data outputs but also to steer computations. Science-based models are being combined with data-driven models to represent complex systems and phenomena. There also is an increasing need for real-time data analytics, which requires large-scale computations to be performed closer to the data and data infrastructures, to adapt to HPC-like modes of operation. These new use cases create a vital need for HPC and BD systems to deal with simulations and data analytics in a more unified fashion.

Open-Knowledge-Network-Workshop-Report-2018-slide

Open Knowledge Network: Summary of the Big Data IWG Workshop

(November 20, 2018)

Technology companies develop proprietary knowledge networks as key business technologies today. However, because these networks are proprietary and expensive to construct, government, academia, small businesses, and nonprofits do not have access to them. In contrast, an open knowledge network (OKN) would be available to all stakeholders, including the researchers who will help push this technology further. An OKN requires a nonproprietary, public–private development effort that spans the entire data science community and will result in an open, shared infrastructure.